Difference between revisions of "Apprenticeship Learning - Inverse Reinforcement Learning (IRL)"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| − | [ | + | [https://www.youtube.com/results?search_query=Inverse+Reinforcement+Machine+Learning+Apprenticeship YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=Inverse+Reinforcement+Machine+Learning+Apprenticeship+machine+learning+ML+artificial+intelligence ...Google search] |
* [[Learning Techniques]] | * [[Learning Techniques]] | ||
| − | ** [[Reinforcement Learning]] | + | ** [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]] |
| − | ** [[Imitation Learning]] | + | ** [[Imitation Learning (IL)]] |
| − | * [[Inside Out - Curious Optimistic Reasoning]] | + | * [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] |
| − | * [[Generative Adversarial Network (GAN)]] | + | * [[Attention]] Mechanism ... [[Transformer]] ... [[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]] |
| − | * [[Connecting Brains]] | + | * [[Symbiotic Intelligence]] ... [[Bio-inspired Computing]] ... [[Neuroscience]] ... [[Connecting Brains]] ... [[Nanobots#Brain Interface using AI and Nanobots|Nanobots]] ... [[Molecular Artificial Intelligence (AI)|Molecular]] ... [[Neuromorphic Computing|Neuromorphic]] ... [[Evolutionary Computation / Genetic Algorithms| Evolutionary/Genetic]] |
| − | * [ | + | * [[Policy]] ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]] |
| − | * [ | + | * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Grok]] | [https://x.ai/ xAI] ... [[Groq]] ... [[Ernie]] | [[Baidu]] |
| + | * [https://arxiv.org/pdf/1806.06877.pdf A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress | Saurabh Arora, Prashant Doshi] 18 Jun 2018 | ||
| + | * [https://arxiv.org/pdf/1805.07687.pdf Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications | Daniel S. Brown, Scott Niekum] 23 Jun 2018 | ||
| + | * [https://analyticsindiamag.com/guide-to-mbirl-model-based-inverse-reinforcement-learning/ Guide to MBIRL – Model Based Inverse Reinforcement Learning | Aishwarya Verma] | ||
| + | <img src="https://149695847.v2.pressablecdn.com/wp-content/uploads/2021/02/IRL.png" width="500"> | ||
Inverse reinforcement learning (IRL) infers/derives a reward function from observed behavior/demonstrations, allowing for policy improvement and generalization. While ordinary "reinforcement learning" involves using rewards and punishments to learn behavior, in IRL the direction is reversed, and a robot observes a person's behavior to figure out what goal that behavior seems to be trying to achieve. | Inverse reinforcement learning (IRL) infers/derives a reward function from observed behavior/demonstrations, allowing for policy improvement and generalization. While ordinary "reinforcement learning" involves using rewards and punishments to learn behavior, in IRL the direction is reversed, and a robot observes a person's behavior to figure out what goal that behavior seems to be trying to achieve. | ||
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Latest revision as of 21:07, 9 April 2024
YouTube search... ...Google search
- Learning Techniques
- Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- Attention Mechanism ... Transformer ... Generative Pre-trained Transformer (GPT) ... GAN ... BERT
- Symbiotic Intelligence ... Bio-inspired Computing ... Neuroscience ... Connecting Brains ... Nanobots ... Molecular ... Neuromorphic ... Evolutionary/Genetic
- Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization (TRPO) ... Policy Gradient (PG) ... Proximal Policy Optimization (PPO)
- Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Grok | xAI ... Groq ... Ernie | Baidu
- A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress | Saurabh Arora, Prashant Doshi 18 Jun 2018
- Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications | Daniel S. Brown, Scott Niekum 23 Jun 2018
- Guide to MBIRL – Model Based Inverse Reinforcement Learning | Aishwarya Verma
Inverse reinforcement learning (IRL) infers/derives a reward function from observed behavior/demonstrations, allowing for policy improvement and generalization. While ordinary "reinforcement learning" involves using rewards and punishments to learn behavior, in IRL the direction is reversed, and a robot observes a person's behavior to figure out what goal that behavior seems to be trying to achieve.
